The works by [1]} and  [2]} empirically demonstrates that when DP-SGD is used on highly imbalanced class data the less represented groups which already have lower accuracy end up losing more utility: “the poor become poorer". They also show that as stricter privacy guarantees are imposed, this gap gets wider.
Our work is similar in nature to these works, however we seek to study another differentially private learning mechanism, named PATE (Private aggregation of Teacher Ensembles,  [3]}) and compare it to DP-SGD in terms of the parity in accuracy of different subgroups.
 [4]} and  [5]} show that if DP was used in the decision making of a fund allocation problem (based on US Census data), smaller districts would end up with comparatively more funding than what they would receive without DP, and larger districts would get less funding.
 [6]} propose differentially private variants of existing bias mitigation techniques for creating fair classifiers. [7]} propose a differentially private learning algorihtm, with fairness constraints imposed.
